Method for determining a ranking of treatment parameters (such as crop protection products) for treating an agricultural field via an efficacy adjustment model based on genetic data

Abstract

A computer-implemented method for generating a control file usable for controlling an agricultural equipment based on at least one treatment parameter, comprising the following steps: (step 1 ) ( 110 ) providing genetic measurement data ( 40 ) of at least one organism which existed or is existing or is expected to exist in the agricultural field, (step 2 ) ( 120 ) providing treatment parameter data ( 42 ) for at least two treatment parameters capable of targeting the at least one organism, (step 3 ) ( 130 ) based on the treatment parameter data ( 42 ), providing first level efficacy data ( 44 ) comprising efficacies (“first level efficacies”) of the at least two treatment parameters relating to the at least one organism on a first level of the taxonomic rank, (step 4 ) ( 140 ) based on the treatment parameter data ( 42 ) and the first level efficacy data ( 44 ), determining a first ranking ( 46 ) of the at least two treatment parameters, (step 5 ) ( 150 ) providing an efficacy adjustment model ( 50 ), (step 6 ) ( 160 ) by modifying the first level efficacy data ( 44 ) based on the genetic measurement data ( 40 ) and the treatment parameter data ( 42 ) via the efficacy adjustment model ( 50 ), obtaining second level efficacy data ( 52 ) comprising efficacies (“second level efficacies”) of the at least two treatment parameters relating to the at least one organism on a second level of the taxonomic rank being below the first level of the taxonomic rank, (step 7 ) ( 170 ) based on the treatment parameter data ( 42 ) and the second level efficacy data ( 52 ), determining a second ranking ( 54 ) of the at least two treatment parameters. (step 8 ) ( 180 ) outputting the highest ranked or user-selected treatment parameter as a control file usable for controlling an agricultural equipment

1 . A computer-implemented method for generating a control file usable for controlling an agricultural equipment based on at least one treatment parameter selected from the group consisting of:
 a) at least one time window for a treatment in an agricultural field,   b) at least one method for a treatment in an agricultural field,   c) at least one product for a treatment in an agricultural field,   d) at least one dose rate for a treatment in an agricultural field, and   e) at least one treatment schedule for a treatment in an agricultural field comprising at least one method or product and a time window for applying the at least one method or product in the agricultural field,   
       wherein the method comprises the following steps:
 (step  1 ) ( 110 ) providing genetic measurement data ( 40 ) of at least one organism which existed or is existing or is expected to exist in the agricultural field, 
 (step  2 ) ( 120 ) providing treatment parameter data ( 42 ) for at least two treatment parameters capable of targeting the at least one organism, 
 (step  3 ) ( 130 ) based on the treatment parameter data ( 42 ), providing first level efficacy data ( 44 ) comprising efficacies (“first level efficacies”) of the at least two treatment parameters relating to the at least one organism on a first level of the taxonomic rank, 
 (step  4 ) ( 140 ) based on the treatment parameter data ( 42 ) and the first level efficacy data ( 44 ), determining a first ranking ( 46 ) of the at least two treatment parameters, 
 (step  5 ) ( 150 ) providing an efficacy adjustment model ( 50 ), 
 (step  6 ) ( 160 ) by modifying the first level efficacy data ( 44 ) based on the genetic measurement data ( 40 ) and the treatment parameter data ( 42 ) via the efficacy adjustment model ( 50 ), obtaining second level efficacy data ( 52 ) comprising efficacies (“second level efficacies”) of the at least two treatment parameters relating to the at least one organism on a second level of the taxonomic rank being below the first level of the taxonomic rank, 
 (step  7 ) ( 170 ) based on the treatment parameter data ( 42 ) and the second level efficacy data ( 52 ), determining a second ranking ( 54 ) of the at least two treatment parameters, and 
 (step  8 ) ( 180 ) outputting the highest ranked or user-selected treatment parameter as a control file usable for controlling an agricultural equipment. 
 
     
     
         2 . The computer-implemented method according to  claim 1 , wherein the obtaining of second level efficacy data ( 52 ) comprises the following steps:
 (step  6   a ) ( 162 ) based on the genetic measurement data ( 40 ) and the treatment parameter data ( 42 ), determining the type of genetics-specific response ( 56 ) of the at least one organism via the efficacy adjustment model ( 50 ),   (step  6   b ) ( 164 ) based on the type of genetics-specific response ( 56 ), modifying the first level efficacy data ( 44 ) via the efficacy adjustment model ( 50 ),   (step  6   c ) ( 166 ) outputting the modified first level efficacy data as second level efficacy data ( 52 ).   
     
     
         3 . The computer-implemented method according to  claim 1 , wherein the obtaining of second level efficacy data ( 52 ) comprises the following steps:
 (step  6   a ) ( 162 ) based on the genetic measurement data ( 40 ) and the treatment parameter data ( 42 ), assigning the type of genetics-specific response ( 56 ) of the at least one organism via the efficacy adjustment model ( 50 ) to one of the following types:
 a) type 1 response ( 58 ): target-site resistance (TSR), 
 b) type 2 response ( 60 ): non-target-site resistance (NTSR), 
 c) type 3 response ( 62 ): no relevant genetics-specific response, 
   (step  6   b ) ( 164 ) wherein in case of type 1 response ( 58 ), the first level efficacy data ( 44 ) are modified via the efficacy adjustment model ( 50 ) in a way that first level efficacies are reduced,
 wherein in case of type 2 response ( 60 ), the first level efficacy data ( 44 ) are modified via the efficacy adjustment model ( 50 ) in a way that that first level efficacies are reduced but reduced in a lower level compared to the case of type 1 response ( 58 ), 
 wherein in case of type 3 response ( 62 ), the first level efficacy data ( 44 ) are modified via the efficacy adjustment model ( 50 ) in a way that these data are validated and/or remain unchanged, 
   (step  6   c ) ( 166 ) outputting the modified first level efficacy data as second level efficacy data ( 52 ).   
     
     
         4 . The computer-implemented method according to  claim 1 , wherein the obtaining of second level efficacy data comprises the following steps:
 (step  6   a ) ( 162 ) based on the genetic measurement data ( 40 ) and the treatment parameter data ( 42 ), assigning the type of genetics-specific response ( 56 ) of the at least one organism via the efficacy adjustment model ( 50 ) to one of the following types:
 a) type 1 response ( 58 ): target-site resistance (TSR), 
 b) type 2 response ( 60 ): non-target-site resistance (NTSR), 
 c) type 3 response ( 62 ): no relevant genetics-specific response, 
   (step  6   b ) ( 164 ) wherein in case of type 1 response ( 58 ), the first level efficacy data ( 44 ) are modified via the efficacy adjustment model ( 50 ) in a way that first level efficacies are set to zero,
 wherein in case of type 2 response ( 60 ), the first level efficacy data ( 44 ) are modified via the efficacy adjustment model ( 50 ) in a way that that first level efficacies are reduced but not set to zero, 
 wherein in case of type 3 response ( 62 ), the first level efficacy data ( 44 ) are modified via the efficacy adjustment model ( 50 ) in a way that these data are validated and/or remain unchanged, 
   (step  6   c ) ( 166 ) outputting the modified first level efficacy data as second level efficacy data ( 52 ).   
     
     
         5 . The computer-implemented method according to  claim 1 , further comprising the following step before (step  1 ) ( 110 ):
 (step  0 ) ( 100 ) taking at least one sample of the at least one organism which existed or is existing or is expected to exist in the agricultural field, conducting a genetic analysis using the at least one sample of the at least one organism, and obtaining therefrom the genetic measurement data ( 40 ) of the at least one organism.   
     
     
         6 . The computer-implemented method according to  claim 1 , further comprising the following step before (step  1 ) ( 110 ):
 (step  0 ) ( 100 ) taking at least one sample of the at least one organism which existed or is existing or is expected to exist in the agricultural field, conducting a genetic analysis using the at least one sample of the at least one organism, and obtaining therefrom the genetic and/or epigenetic information of the at least one organism, wherein the genetic analysis is based on at least one of the technologies selected from the group consisting of sequencing technologies—such as Sanger sequencing, next generation sequencing, pyrosequencing, nanopore sequencing, GenapSys sequencing, sequencing by ligation (SOLID sequencing), single-molecule real-time sequencing, Ion semiconductor (Ion Torrent sequencing) sequencing, sequencing by synthesis (Illumina), combinatorial probe anchor synthesis (cPAS-BGI/MGI)—, nanopore technology, microarray technology, graphene biosensor technology, PCR (polymerase chain reaction) technology, fast PCR technology, and other DNA/RNA amplification technologies such as isothermal amplification—such as LAMP (Loop mediated amplification), RPA (Recombinase Polymerase Amplification), Nucleic Acid Sequenced Based Amplification (NASBA) and Transcription Mediated Amplification (TMA)—, as well as epigenetic analysis such as DNA methylation, DNA-Protein interaction analysis, and Chromatin accessibility analysis.   
     
     
         7 . The computer-implemented method according to  claim 1 , wherein timeframe between sample-taking and the provision of the genetic measurement data ( 40 ) is from 1 seconds to 5 days. 
     
     
         8 . The computer-implemented method according to  claim 1 , wherein the at least one organism is a harmful organism selected from the group consisting of: weeds, fungi, viruses, bacteria, insects, arachnids, nematodes, mollusks, birds, and rodents. 
     
     
         9 . The computer-implemented method according to  claim 1 , wherein the at least one organism is a beneficial organism selected from the group consisting of: beneficial plants, fungi, viruses, bacteria, insects, arachnids, nematodes, mollusks, birds, rodents, and protozoa. 
     
     
         10 . The computer-implemented method according to  claim 1 , wherein the at least one organism is an agricultural crop species grown, sown, planned to be grown, or planned to be sown in the agricultural field. 
     
     
         11 . The computer-implemented method according to  claim 1 , wherein the highest ranked treatment parameter will be outputted as a control file for an agricultural equipment. 
     
     
         12 . A data processing system comprising means for carrying out the computer-implemented method according to  claim 1 . 
     
     
         13 . A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the computer-implemented method according to  claim 1 . 
     
     
         14 . A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the computer-implemented method according to  claim 1 . 
     
     
         15 . Use of the highest ranked treatment parameter determined by the computer-implemented method according to  claim 1  for controlling an agricultural equipment.